Instructions to use gvadhul/byte-gpt2-2layer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use gvadhul/byte-gpt2-2layer with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="gvadhul/byte-gpt2-2layer")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("gvadhul/byte-gpt2-2layer") model = AutoModelForCausalLM.from_pretrained("gvadhul/byte-gpt2-2layer") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use gvadhul/byte-gpt2-2layer with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "gvadhul/byte-gpt2-2layer" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "gvadhul/byte-gpt2-2layer", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/gvadhul/byte-gpt2-2layer
- SGLang
How to use gvadhul/byte-gpt2-2layer with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "gvadhul/byte-gpt2-2layer" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "gvadhul/byte-gpt2-2layer", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "gvadhul/byte-gpt2-2layer" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "gvadhul/byte-gpt2-2layer", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use gvadhul/byte-gpt2-2layer with Docker Model Runner:
docker model run hf.co/gvadhul/byte-gpt2-2layer
| """Minimal byte tokenizer: token id == UTF-8 byte value, everything in [0, 256). | |
| Mirrors the UTF8Tokenizer design principle (no out-of-range ids; special roles | |
| ride on C0 control bytes) without an external dependency. Pad = NUL (byte 0x00). | |
| """ | |
| from transformers import PreTrainedTokenizer | |
| class ByteTokenizer(PreTrainedTokenizer): | |
| model_input_names = ["input_ids", "attention_mask"] | |
| def __init__(self, pad_token="\x00", **kwargs): | |
| # Map pad to an existing byte id (0) BEFORE super().__init__, so it is | |
| # NOT allocated a fresh id at 256. id == byte stays true for everything. | |
| from transformers import AddedToken | |
| self._added_tokens_decoder = { | |
| 0: AddedToken(pad_token, special=True) if isinstance(pad_token, str) else pad_token | |
| } | |
| super().__init__(pad_token=pad_token, **kwargs) | |
| def vocab_size(self): | |
| return 256 | |
| def get_vocab(self): | |
| vocab = {chr(i): i for i in range(256)} | |
| vocab.update(self.added_tokens_encoder) | |
| return vocab | |
| def _tokenize(self, text): | |
| return [chr(b) for b in text.encode("utf-8")] | |
| def _convert_token_to_id(self, token): | |
| return ord(token) if len(token) == 1 and ord(token) < 256 else self.unk_token_id | |
| def _convert_id_to_token(self, index): | |
| return chr(index) | |
| def convert_tokens_to_string(self, tokens): | |
| return bytes(ord(t) for t in tokens).decode("utf-8", errors="replace") | |